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| 1 | +#!/usr/bin/env python |
| 2 | +""" |
| 3 | +Download the BedMachine topography |
| 4 | +---------------------------------- |
| 5 | +1. Go tohttps://nsidc.org/data/nsidc-0756/versions/3 |
| 6 | +2. Click on "HTTPS File System" |
| 7 | +3. Log in or create and account. |
| 8 | +5. Under "1970.01.01" choose "BedMachineAntarctica-v3.nc" |
| 9 | +
|
| 10 | +On Anvil or Chrysalis, BedMachine Antarctica v3 is available so create a |
| 11 | +symlink with: |
| 12 | +ln -s /lcrc/group/e3sm/public_html/mpas_standalonedata/mpas-ocean/bathymetry_database/BedMachineAntarctica-v3.nc |
| 13 | +""" |
| 14 | + |
| 15 | +import shutil |
| 16 | + |
| 17 | +import matplotlib.pyplot as plt |
| 18 | +import numpy as np |
| 19 | +import shapely |
| 20 | +import xarray as xr |
| 21 | +from pyproj import Transformer |
| 22 | + |
| 23 | +from geometric_features import GeometricFeatures, FeatureCollection |
| 24 | +from geometric_features.feature_collection import _round_coords |
| 25 | +from geometric_features.utils import write_feature_names_and_tags |
| 26 | + |
| 27 | + |
| 28 | +def get_longest_contour(contour_value, author): |
| 29 | + |
| 30 | + def stereo_to_lon_lat(x, y): |
| 31 | + lat, lon = transformer.transform(x, y) |
| 32 | + return lon, lat |
| 33 | + |
| 34 | + with xr.open_dataset('BedMachineAntarctica-v3.nc') as ds: |
| 35 | + # the bed but only in the open ocean or under floating ice |
| 36 | + bed = xr.where(np.logical_or(ds.mask == 0, ds.mask == 3), |
| 37 | + ds.bed, 0.).values |
| 38 | + x = ds.x.values |
| 39 | + y = ds.y.values |
| 40 | + |
| 41 | + # plot contours |
| 42 | + plt.figure() |
| 43 | + cs = plt.contour(x, y, bed, (contour_value,)) |
| 44 | + paths = cs.allsegs[0] |
| 45 | + |
| 46 | + path_lengths = [len(paths[i]) for i in range(len(paths))] |
| 47 | + ilongest = np.argmax(path_lengths) |
| 48 | + |
| 49 | + v = paths[ilongest] |
| 50 | + x = v[:, 0] |
| 51 | + y = v[:, 1] |
| 52 | + |
| 53 | + # the starting index should be the point closest to the positive x axis |
| 54 | + mask = x > 0. |
| 55 | + indices = np.arange(y.shape[0])[mask] |
| 56 | + index = np.argmin(np.abs(y[mask])) |
| 57 | + first = indices[index] |
| 58 | + x = np.append(x[first:], x[:first]) |
| 59 | + y = np.append(y[first:], y[:first]) |
| 60 | + |
| 61 | + # Antarctic stereographic to lat/lon |
| 62 | + transformer = Transformer.from_crs('epsg:3031', 'epsg:4326') |
| 63 | + |
| 64 | + transect = shapely.geometry.LineString([(i[0], i[1]) for i in zip(x, y)]) |
| 65 | + |
| 66 | + # cut a square out at around the first point at 90 degrees longitude to |
| 67 | + # prevent the shape from being a closed loop |
| 68 | + width = 30e3 |
| 69 | + x0 = x[0] |
| 70 | + y0 = y[0] |
| 71 | + square = shapely.geometry.Polygon([(x0 - width, y0 - width), |
| 72 | + (x0 + width, y0 - width), |
| 73 | + (x0 + width, y0 + width), |
| 74 | + (x0 - width, y0 + width), |
| 75 | + (x0 - width, y0 - width)]) |
| 76 | + |
| 77 | + difference = transect.difference(square) |
| 78 | + |
| 79 | + # cut a tiny weged out to break the shape into 2 at the antimeridian |
| 80 | + epsilon = 1e-14 |
| 81 | + minY = np.amin(y) |
| 82 | + wedge = shapely.geometry.Polygon([(epsilon, minY), |
| 83 | + (epsilon**2, -epsilon), |
| 84 | + (0, epsilon), |
| 85 | + (-epsilon**2, -epsilon), |
| 86 | + (-epsilon, minY), |
| 87 | + (epsilon, minY)]) |
| 88 | + |
| 89 | + difference = difference.difference(wedge) |
| 90 | + |
| 91 | + transect_latlon = shapely.ops.transform(stereo_to_lon_lat, difference) |
| 92 | + |
| 93 | + plt.figure() |
| 94 | + for geom in transect_latlon.geoms: |
| 95 | + x, y = geom.xy |
| 96 | + plt.plot(x, y) |
| 97 | + |
| 98 | + fc = FeatureCollection() |
| 99 | + |
| 100 | + geometry = shapely.geometry.mapping(transect_latlon) |
| 101 | + # get rid of the wedge again by rounding the coordinates |
| 102 | + geometry['coordinates'] = _round_coords(geometry['coordinates']) |
| 103 | + |
| 104 | + fc.add_feature( |
| 105 | + {'type': 'Feature', |
| 106 | + 'properties': {'name': f'Antarctic isobath at {contour_value} m', |
| 107 | + 'author': author, |
| 108 | + 'object': 'transect', |
| 109 | + 'component': 'ocean', |
| 110 | + 'tags': 'Antarctic'}, |
| 111 | + 'geometry': geometry}) |
| 112 | + |
| 113 | + return fc |
| 114 | + |
| 115 | + |
| 116 | +def main(): |
| 117 | + xylar = 'Xylar Asay-Davis' |
| 118 | + |
| 119 | + # make a geometric fieatures object that knows about the geometric data |
| 120 | + # cache up a couple of directories |
| 121 | + gf = GeometricFeatures('../../geometric_data') |
| 122 | + |
| 123 | + fc = get_longest_contour(contour_value=-1000., author=xylar) |
| 124 | + |
| 125 | + # "split" these features into individual files in the geometric data cache |
| 126 | + gf.split(fc) |
| 127 | + |
| 128 | + # update the database of feature names and tags |
| 129 | + write_feature_names_and_tags(gf.cacheLocation) |
| 130 | + # move the resulting file into place |
| 131 | + shutil.copyfile('features_and_tags.json', |
| 132 | + '../../geometric_features/features_and_tags.json') |
| 133 | + |
| 134 | + plt.show() |
| 135 | + |
| 136 | + |
| 137 | +if __name__ == '__main__': |
| 138 | + main() |
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